Statistics for Genomic Data Science
link 来源:www.coursera.org
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assignment 等级:入门
chat_bubble_outline 语言:英语
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关于内容

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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课程大纲

  • Week 1 - Module 1
    This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
  • Week 2 - Module 2
    This week we will cover preprocessing, linear modeling, and batch effects.
  • Week 3 - Module 3
    This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
  • Week 4 - Module 4
    In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.
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教师

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

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内容设计师

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

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